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» Maintaining variance and k-medians over data stream windows
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ICDM
2006
IEEE
161views Data Mining» more  ICDM 2006»
13 years 11 months ago
STAGGER: Periodicity Mining of Data Streams Using Expanding Sliding Windows
Sensor devices are becoming ubiquitous, especially in measurement and monitoring applications. Because of the real-time, append-only and semi-infinite natures of the generated se...
Mohamed G. Elfeky, Walid G. Aref, Ahmed K. Elmagar...
ISMIS
2009
Springer
13 years 11 months ago
Novelty Detection from Evolving Complex Data Streams with Time Windows
Abstract. Novelty detection in data stream mining denotes the identification of new or unknown situations in a stream of data elements flowing continuously in at rapid rate. This...
Michelangelo Ceci, Annalisa Appice, Corrado Loglis...
ICDE
2012
IEEE
291views Database» more  ICDE 2012»
12 years 4 months ago
Efficiently Monitoring Top-k Pairs over Sliding Windows
Top-k pairs queries have received significant attention by the research community. k-closest pairs queries, k-furthest pairs queries and their variants are among the most well stu...
Zhitao Shen, Muhammad Aamir Cheema, Xuemin Lin, We...
KDD
2010
ACM
300views Data Mining» more  KDD 2010»
13 years 9 months ago
Mining top-k frequent items in a data stream with flexible sliding windows
We study the problem of finding the k most frequent items in a stream of items for the recently proposed max-frequency measure. Based on the properties of an item, the maxfrequen...
Hoang Thanh Lam, Toon Calders
ICDE
2009
IEEE
278views Database» more  ICDE 2009»
14 years 6 months ago
Probabilistic Skyline Operator over Sliding Windows
Abstract-- Skyline computation has many applications including multi-criteria decision making. In this paper, we study the problem of efficient processing of continuous skyline que...
Wenjie Zhang, Xuemin Lin, Ying Zhang, Wei Wang 001...